DC 6:36: “Look unto me in every thought; doubt not, fear not.”
“As an apostle of the Lord Jesus Christ, I invoke these blessings upon you, that as you look to the Savior and trust in Him, you will be blessed with hope to overcome perplexity, with spiritual settledness to cut through commotion, with ears to hear and a heart to always remember the word of the Lord, and with the discernment to see things as they really are.”
David A. Bednar - BYU Speeches, April 16th, 2021
Remember that you grade your own submitted exercises using the rubric specified in each posted exercise solution, adding comments (directly for R scripts or using the comment feature for Word documents) where their solution differs from the posted solution.
# Find the customers who have spent the most recently. customer_data |> left_join(store_transactions, join_by(customer_id)) |> # -1: I forgot to filter for the South and create a new age column. select(age, gender:region, dec_2018) |> arrange(desc(dec_2018)) |> slice(1:3)
Remember that summarizing data is initially all about discovery, the heart of exploratory data analysis.
How we summarize depends on whether the data is discrete or continuous.
library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ── ## ✔ dplyr 1.1.4 ✔ readr 2.1.5 ## ✔ forcats 1.0.0 ✔ stringr 1.5.1 ## ✔ ggplot2 3.5.1 ✔ tibble 3.2.1 ## ✔ lubridate 1.9.3 ✔ tidyr 1.3.1 ## ✔ purrr 1.0.2 ## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ── ## ✖ dplyr::filter() masks stats::filter() ## ✖ dplyr::lag() masks stats::lag() ## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
Can you identify any discrete variables? What are their data types in R?
customer_data <- read_csv("customer_data.csv")
## Rows: 10531 Columns: 13 ## ── Column specification ──────────────────────────────────────────────────────── ## Delimiter: "," ## chr (8): gender, married, college_degree, region, state, review_time, review... ## dbl (5): customer_id, birth_year, income, credit, star_rating ## ## ℹ Use `spec()` to retrieve the full column specification for this data. ## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
An important statistic for a discrete variable is a count.
customer_data |> count(region)
## # A tibble: 4 × 2 ## region n ## <chr> <int> ## 1 Midwest 1101 ## 2 Northeast 3224 ## 3 South 1111 ## 4 West 5095
How would I get a count by both region and college_degree (i.e., a cross-tab)?
customer_data |> count(region, college_degree)
## # A tibble: 8 × 3 ## region college_degree n ## <chr> <chr> <int> ## 1 Midwest No 229 ## 2 Midwest Yes 872 ## 3 Northeast No 640 ## 4 Northeast Yes 2584 ## 5 South No 891 ## 6 South Yes 220 ## 7 West No 989 ## 8 West Yes 4106
{ggplot2} provides a consistent grammar of graphics built with layers.
Plot our first summary (note how + is different from |>).
customer_data |> count(region) |> ggplot(aes(x = region, y = n)) + geom_col()
Visualize our second summary by adding the aesthetic fill = college_degree.
The geom position argument of the geom_col() function is set to stack by default. Try fill instead.
customer_data |> count(region, college_degree) |> ggplot(aes(x = region, y = n, fill = college_degree)) + geom_col()
customer_data |> count(region, college_degree) |> ggplot(aes(x = region, y = n, fill = college_degree)) + geom_col(position = "fill")
Facets allow us to visualize by another discrete variable. For example, is this relationship different depending on gender?
customer_data |> count(region, college_degree, gender) |> ggplot(aes(x = region, y = n, fill = college_degree)) + geom_col(position = "fill") + facet_wrap(~ gender)
It’s no longer a count on the y-axis. Let’s change the labels.
customer_data |>
count(region, college_degree, gender) |>
ggplot(aes(x = region, y = n, fill = college_degree)) +
geom_col(position = "fill") +
facet_wrap(~ gender) +
labs(
title = "Proportion of Customers with College Degrees by Region and Gender",
subtitle = "Based on 10,531 Customers in the CRM Database",
x = "Region",
y = "Proportion"
)
What about the legend? And these colors?
customer_data |>
count(region, college_degree, gender) |>
ggplot(aes(x = region, y = n, fill = college_degree)) +
geom_col(position = "fill") +
facet_wrap(~ gender) +
labs(
title = "Proportion of Customers with College Degrees by Region and Gender",
subtitle = "Based on 10,531 Customers in the CRM Database",
x = "Region",
y = "Proportion"
) +
scale_fill_manual(
name = "College Degree",
values = c("royalblue", "darkblue")
)
Text data is also discrete but it is unstructured.
What sort of structure might we impose on text data so we can visualize it?
We can use unnest_tokens() to tokenize the text (i.e., split it into individual words or tokens).
review_data <- customer_data |> select(customer_id, review_text) |> tidytext::unnest_tokens(word, review_text) review_data
## # A tibble: 165,510 × 2 ## customer_id word ## <dbl> <chr> ## 1 1001 everything's ## 2 1001 fine ## 3 1002 <NA> ## 4 1003 <NA> ## 5 1004 <NA> ## 6 1005 i ## 7 1005 looked ## 8 1005 all ## 9 1005 over ## 10 1005 the ## # ℹ 165,500 more rows
With the text data tokenized, we can compute counts just like other discrete data.
review_data |> count(word) |> arrange(desc(n))
## # A tibble: 10,178 × 2 ## word n ## <chr> <int> ## 1 the 7512 ## 2 <NA> 7373 ## 3 and 4633 ## 4 i 4486 ## 5 a 4176 ## 6 to 3949 ## 7 it 3581 ## 8 for 2531 ## 9 is 2419 ## 10 of 2106 ## # ℹ 10,168 more rows
Missing values are (and should be) encoded as NA.
review_data <- review_data |> drop_na(word) review_data
## # A tibble: 158,137 × 2 ## customer_id word ## <dbl> <chr> ## 1 1001 everything's ## 2 1001 fine ## 3 1005 i ## 4 1005 looked ## 5 1005 all ## 6 1005 over ## 7 1005 the ## 8 1005 internet ## 9 1005 to ## 10 1005 find ## # ℹ 158,127 more rows
Commonly used words aren’t very informative and are referred to as stop words.
tidytext::stop_words
## # A tibble: 1,149 × 2 ## word lexicon ## <chr> <chr> ## 1 a SMART ## 2 a's SMART ## 3 able SMART ## 4 about SMART ## 5 above SMART ## 6 according SMART ## 7 accordingly SMART ## 8 across SMART ## 9 actually SMART ## 10 after SMART ## # ℹ 1,139 more rows
This is just a data frame, and we know how to join data frames!
An anti join keeps rows that don’t have matching IDs and just the columns from the “left” data frame. (It’s nearly the opposite of an inner join.)
review_data <- review_data |> anti_join(tidytext::stop_words, join_by(word)) review_data |> count(word) |> arrange(desc(n))
## # A tibble: 9,552 × 2 ## word n ## <chr> <int> ## 1 fit 403 ## 2 product 378 ## 3 easy 338 ## 4 quality 330 ## 5 nice 324 ## 6 bag 287 ## 7 price 281 ## 8 time 278 ## 9 love 264 ## 10 size 247 ## # ℹ 9,542 more rows
review_data |> count(word) |> arrange(desc(n)) |> ggplot(aes(x = word, y = n)) + geom_col()
What can we do to make this plot readable?
Unlike a character variable, a factor can include information about order.
levels are numeric values that encode order.labels are the character string associated with each level.review_data |> count(word) |> arrange(desc(n)) |> slice(1:10) |> mutate(word = fct_reorder(word, n)) |> ggplot(aes(x = n, y = word)) + geom_col()
Summary
Next Time
Supplementary Material
Artwork by @allison_horst
In RStudio on Posit Cloud, create a new R script and do the following.
customer_data using the functions we’ve covered.|>, and use the demonstrated style (e.g., variable names, spacing within functions).